Reconstructed from Danny Postma's talk How I Built My Own AgentOS on Claude's Agent SDK (So You Can Too) (2026). This document is both a product spec for a human and an implementation prompt for an AI coding agent. Build exactly this system. Do not invent features that are not specified here.

Role contracts and prompts in this file are reconstructed from the talk, not his verbatim files. Mark every reconstructed prompt in code comments and docs as such.

• Do not subscribe to every service at once. Most free credits expire quickly. Test them one by one and move to the next when your current credits run out.
• Register, verify your account, and claim your credits. Follow each platform’s documentation to connect your coding tools.
• Keep your API keys private. Do not share them publicly. (save or star this gist for future update)

Compiled by Adityarup Laha (MB2402; BS2124) using publicly available information (and the Wayback Machine!). The full dump as of February 26, 2026, 03:30 IST is available at: https://public-files.adityaruplaha.me/isi-placement-archive/2026-02-26_isi-placement-archive.zip

Individual links are provided below for provenance.

A Pen by Fishhrrr on CodePen.

License.

A small, dependency-free redeploy script for Node.js/NestJS apps running under PM2 on a VPS. Pulls the latest code, installs deps, builds, and reloads the app without dropping in-flight requests.

1. Checks the deploy directory for uncommitted local changes and aborts if found — prevents accidentally overwriting a manual hotfix someone made directly on the server.
2. Fetches and checks out the target branch (main by default).
3. Runs npm ci (not npm install) — installs exactly what's in package-lock.json, no surprise version bumps in production.

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

Two real deployment paths are covered here, side by side:

• Path A — Docker (recommended for production, what most teams actually run)
• Path B — Bare-metal clone + PM2 (fine for solo VPS, small projects, or when you can't run Docker)

Pick one. Don't mix them on the same server unless you know why.

Please make me a rich, interactive explanation of the specified code change.

It should have these sections:

Ocean Toolkit, which was distributed for free during the 3rd week of May 2026, is an ocean rendering asset with excellent visual quality.
However, because the asset was originally released quite a long time ago, it only supports the Built-in Render Pipeline.